Category Archives: Ad Ecosystem

The Fifth Wave Of Ad Tech: Privileged Programmatic

By Eric Picard (originally published on Adexchanger.com Friday, March 10th, 2017)

The first seven years of the programmatic revolution were driven by three major efforts.

It began with the creation and propagation of the massive new infrastructure needed to support real-time bidding. That was followed by the connection of all demand to all supply in the programmatic infrastructure. New ad products, formats and platforms then emerged, built on top of this new infrastructure.

This was a significant revolution – what I’ve called the third and fourth waves of ad technology. We’re now entering a fifth wave: privileged programmatic.

As the programmatic ecosystem matures, we’re seeing massive adoption of these new tools and technologies by the largest advertisers and media agencies now spending at scale. During the first seven years or so, many ad networks procured and resold media and some large marketer early adopters broke ground – many of which are now reaping the dividends.

But the very largest budgets are now coming into programmatic, and the game is changing. To illustrate the change, let’s talk about the historical evolution that the financial markets went through as they hit their maturity threshold during the rise of electronic trading.

Lessons From High-Speed Traders

In the highly recommended book “Dark Pools: The Rise of the Machine Traders and the Rigging of the US Stock Market,” by Scott Patterson, there is a clear narrative that will start to feel familiar to those working in programmatic media.

When the electronic markets were created, the early winners were typically hedge funds established and managed by the same humans who built the electronic market infrastructure. They knew that traders that responded the fastest to auctions could get significant advantage over other participants. Thus began the high-speed trading (HST) revolution. High-speed traders made millions of dollars a day on high-volume trades at very high speeds.

As the market matured, large traditional stock market players entered the electronic trading business and had their lunch eaten by the upstart high-speed traders. They found this to be unacceptable. The basic logic was, “If I’m spending billions of dollars a year on your electronic exchange, I need some privilege that gets me ahead of these little upstarts who have ‘know-how’ but are tiny players compared to me.”

The biggest players went to the exchanges and demanded privileged bidding mechanisms to allow them to win in the auction even if another player bid higher or bid first. They removed the advantage built in by the high-speed traders.

Nobody warned the HST companies. Within weeks in some cases, many simply went out of business. They had no idea what happened, but knew they suddenly weren’t winning in the auction. Eventually a few found out that the unpublished bid mechanisms that allowed them and the large brokerages to win in the auction had been uncovered and made available more broadly. But most of the damage was already done.

Privileged Programmatic

Privilege in an auction environment is not necessarily a bad thing. Much like the RTB exchanges in advertising, the electronic markets were seen as the great equalizers – fair unbiased auctions – but the reality is that the HST companies had their own type of advantage based on infrastructure knowledge. A real business argument can be made that buyers spending vast amounts of money should be able to negotiate for privilege with the sellers. That’s exactly what is happening in programmatic advertising.

Have you noticed that many of the biggest early players in programmatic have come upon hard times? Suddenly algorithms that were designed to provide advertisers with performance while still stripping off big dollars via an arbitrage model stopped working. Why?

Over the last few years we’ve seen the massive adoption of new privileged mechanisms in programmatic. Whether we discuss private marketplaces (PMPs), header bidding, first-look or programmatic guaranteed, they are predictable artifacts of the maturation of the programmatic marketplace. And don’t let any early knowledge you’ve gathered on these mechanisms create a false sense of comfort – PMPs from three years ago often look nothing like the configuration seen today. These mechanisms are not created equally.

For publishers, this maturation is very good news. Many large publishers viewed programmatic as a “rush to the bottom” in the early days and now see programmatic mechanisms bringing balance back to the marketplace.

Many publishers expressed frustration as programmatic created for the first time in digital media an information asymmetry that favored advertisers. Publishers had no idea why advertisers bought media from them over the open exchange, and now with these privileged mechanisms, the conversation has moved back to media buying and sales teams are empowered to negotiate and structure deals that drive customer value.

The hallmark of the first seven years of programmatic was a bottoms-up reinvention of buying based on data-driven decisioning and performance – and the biggest lever on performance was price of inventory. Early adopters were astounded to find their desired audiences for a low cost on the exchanges, even at the same publisher sites where they were simultaneously executing direct buys at much higher prices.

But those same savvy early adopters who realized huge discounts by buying the same users on the same publishers over the open exchange saw the writing on the wall. They recognized that prices were rising on the best users as the competition in the auction rose – since unsurprisingly, the same users seemed to be of interest to all advertisers in the same sector.

The savviest advertisers went directly to publishers and made PMP deals to access inventory with mechanisms that gave them advantage over their competition – which is also known as privilege. By putting their PMPs in increasingly higher priority within the ad server, setting up fixed-rate, variable and hybrid-rate deals and using new tools like header bidding, the most knowledgeable buyers stayed ahead of the competition. Publishers saw that these new mechanisms drove much higher CPMs, in many cases higher than direct buys, and importantly gave them insight into why advertisers bought from them. Eventually, the very most desirable audiences on the largest and best publishers evaporated out of the open auction.

The market is tipping over on itself – with open auctions being relegated more and more to purely direct-response advertisers that are not selective about which publishers their advertising runs on. For large brands, especially those spending large budgets, which also tend to be those that care deeply about running ads on high-quality publishers, things have gotten a lot more sophisticated.

Programmatic is no longer about low-cost inventory; it is now the infrastructure for transaction where the buyer and seller are handshaking and establishing connections to the consumers that brands need to reach. Programmatic is the mechanism to bind together the new tools that empower the advertiser to take control of their audiences and apply real science to the art of advertising. Publishers now can gain insight from working through these mechanisms rather than being left in the dark.

Sophisticated publishers already know this – and are driving programmatic elements or line items in their core I/Os as part of their direct business. On the buy side, the trend is for agencies to blow up their trading desks and embed programmatic buyers into direct buying teams.

This is a clear wake-up call for publishers that are still not treating programmatic as part of their direct sales or which haven’t changed sales compensation to remove channel conflict. Same for advertisers and media agencies who are segregating their programmatic buyers from their direct buyers.

Deal design has gotten extremely sophisticated, and the trend is toward increased sophistication, not simplification. If you are driving programmatic sales at a publisher and your deals are very one-dimensional, you’re probably missing something.

If you’re buying programmatically today and haven’t analyzed the core audiences you’re reaching over the open exchange, broken out by publishers that you’re also buying directly from, you’re behind your competitors.

And if you’re a marketer, question your media partners about all of these things. You have time, but not very much.


4 Comments

  1. In effect, were seeing “networks” appear that advertisers use. Yes! ad networks are back but the publishers are acting like their own middleman. The buyers can now group together publisher networks and create their own ecosystem of their own choosing. Tis a fun time to see the same philosophy repeat itself
    • Gerard, it’s not quite the same thing, neither philosophically nor structurally. Ad networks were arbitrage mechanisms designed to extract money from the ecosystem. This is a direct relationship between buyer and seller. The seller and buyer pay only technology fees and deal with the negotiation costs.
  2. Eric,Great piece. We definitely live in interesting times. The pace of change is such that the costs of both technology, talent, and training to keep up, may out weigh the benefits gained. Are we as an industry encouraging brands to sit on the sideline and wait for the dust to settle? Also, and most importantly, what do you see as wave six?

    Reply

    • Eric Picard March 20, 2017
      Hey R.J. This is a trend that is finally culminating after a long incubation. I don’t see it as a time for anyone to sit on the sidelines – this is the holy grail we’ve all been waiting for: Publishers are empowered to sell and build value-based relationships with buyers. Advertisers get value from their customer data investments and the ability to intelligently decide who to reach, at what frequency, and how much to pay for that exposure. Wave six? I just got Wave five out to you – let’s start there.

Ad Tech Vet Eric Picard Joins Pandora As VP Of Ad Product Management

Originally Published on AdExchanger.com – Wednesday, December 21st, 2016

Pandora is increasing its bet on ad tech.

The streaming music platform will bring on Eric Picard as VP of ad product management to continue building out display and video products and lead its dive into programmatic audio.

Picard is a longtime ad tech executive. In 1997, he launched Bluestreak, one of the first companies to create the rich media formats that are standard in digital today. Since then, he’s launched numerous ad tech startups, led ad product strategy for Microsoft and, most recently, was VP of omnichannel media for MediaMath. Picard joined MediaMath via its acquisition of Rare Crowds, a programmatic platform he founded in 2012.

“I’ve been in ad tech my entire career,” he said. “I have played roles in teams across pretty much every aspect of the space.”

In audio, and at Pandora specifically, Picard sees an opportunity to “participate in such a large marketplace for an ad media type that hasn’t been fully explored yet.”

“There aren’t too many places in the market to go that are nearly as exciting as the marketplace that Pandora has built for audio, display and video ads,” he said.

In his role, Picard will lead a team of 15 to 20 product managers focused on building and optimizing ad products. He plans to grow that team during his tenure.

Pandora has been bullish on programmatic display but hasn’t yet begun selling its in-stream audio ads programmatically. Picard will likely have a big part in pioneering that in 2017.

“I’ve been deeply involved in the next generation of platforms and methodologies, what we loosely call programmatic,” he said. “You can imagine that we’re thinking a lot about a lot of those things.”

Pandora offers an opportunity to innovate in an area of ad tech that’s still nascent.

“Figuring out the future of audio is obviously the enticement,” he said.

 

Get creative with hyperlocal targeting

By Eric Picard & Max Dowaliby (Originally Published on Imedia – December 16, 2015)

Hyperlocal targeting is the shiniest method of delivering advertising to consumers based on their exact location.  This is geo-targeting taken to the logical conclusion of every person carrying a GPS locator on their person wherever they go, even though GPS is only one method of determining location. The introduction of location data into mobile advertising has allowed advertisers to leverage the always-on, always-connected mobile device as an indicator of location. This has driven hyperlocal targeting to become one of the fastest growing mechanisms to capture dollars allocated to local advertising.

According to Borrell Associates, 42 percent of all local advertising is expected to be digital in 2015, totaling over $47 billion. Sixty-one percent of smartphone users say that they are more likely to buy from mobile sites and applications if they customize content or information to the current location of the user.

There are many complexities to local advertising that have not been sorted out, even with these advances.  For years, analysts have been talking about the coming transition of local dollars to digital, but it is possible that hyperlocal targeting could change this. The main issues in the transition of local to digital has been the so-called “local independents” — the “mom-and-pop” shops — which are the standalone companies that make up the vast majority of local businesses.  For these companies, local for years has meant Yellow Pages and newspapers, and, for the larger ones, radio and potentially local television. Mainly this has been held back due to creative production, as these small businesses don’t have the means to create advertising to fit the needs of the digital space.

The “national-local” advertisers — the brands with local presence — ranging from quick-service restaurants to retailers are the main drivers of adoption of digital. Until mobile location data really became actionable, there was still little reason for the local dollars of the national brands to transition to digital — as geo-targeting was seen as too vague, and the creative value propositions were not quite strong enough. Things are changing.

Hyperlocal targeting is not a simple mechanism for identifying or targeting users. It’s more of an overall set of services for leveraging highly accurate, fresh, and relevant data about a user in order to make the best decision matching the ad opportunity to the consumer, based on their exact location. Let’s explore hyperlocal location targeting (what most people are referring to when they say hyperlocal targeting):

Advertisers have been able to do some sort of location targeting for years now. Targeting based on city, DMA, or zip code have been well-used and well-performing tactics. However, the real challenge here is getting more granular than a zip code. Since mobile phones provide signals that allow us to achieve incredibly granular information, the mass adoption and nonstop usage of these devices has — in many ways — solved the problem for us.

Hyperlocal location targeting refers to the ability to be able to target small areas or “geo-fences,” including radius (general distance from a target location) and polygonal geo-fences (a shape drawn on a map). Both of these mechanisms have uses for targeting users; for instance, a message could be sent to users when inside a certain radius of a specifically targeted destination. An example: A retailer might want to send “message A” when a user is within 2 miles, “message B” when a user is within 1 mile, and “message C” when a user is within 100 meters. This can be an extremely powerful tool to drive foot traffic or engagement. This location information also allows advertisers to provide relevant, and contextually aware content to users. In the case of a polygon, a quick-service restaurant that delivers food might have very specific streets that become a boundary for where they deliver from one location versus another — and radius simply won’t solve for this.

We now have incredibly accurate signals by which we should be able to target users, but there are still some key challenges when trying to leverage this data. Arguably the most important is the accuracy of this information. Depending on where the location information is coming from (browser, in-app, carrier, etc.), the precision varies greatly. Location information is conveyed via latitude-longitude coordinate pairs, and, as such, can vary in degrees of precision. Carrier-provided location data is often only accurate to the area that an individual cell tower provides service to, whereas in-app provided location data can be extremely accurate and place a user inside a retail store, or even in a certain part of a retail store. There is also a large amount of (usually) unintentional location fraud. This refers to revered latitude-longitude pairs, missing coordinates, or centroids (a central point in a city, state, country, etc.). There are numerous location targeting partners who cleanse and validate location data to help this problem, but it remains an issue that cannot be ignored.

Freshness of the location information is important in hyperlocal location targeting. It is critical that a user be messaged when they are physically at a certain location, not when they were there five minutes ago. One of the challenges of dealing with location information is that this data cannot be cached the same way most information about a user can be. Location is fluid, and users are constantly moving. This makes location data at scale an incredible amount of information to process.

However, when these challenges are overcome, there results are worth it. A quality hyperlocal campaign can provide incredible utility and relevance to a user. Messaging a user at the right place and right time works, we know that. It’s all about the execution. Users are clamoring for this kind of utility. Eighty percent of Google searches that included the term “near me” were from mobile devices in Q4 2014. Even more importantly, the prevalence of the term “near me” is up 34 times since 2011. Users now want — if not demand — relevant information and experiences based on where they are. Hyperlocal is a buzz word, and for good reason. Let’s just make sure we use it to its fullest capabilities. Get creative!

Viewability in programmatic media: An honest conversation

By Eric Picard, Ari Buchalter & Mike Monaco (Originally Published on iMedia – October 1, 2015)

An advertisement — whether it’s a banner ad, video pre-roll, mobile pop-up, or anything else — needs to be viewed by a consumer for it to be valuable for the company placing the ad. But does that mean advertisers should only pay for ads that are “viewable”? The answer is complicated.

The Interactive Advertising Bureau (IAB) defines a viewable ad as at least 50 percent in-view for at least one second. Even with a standard in place, vendors measuring performance use their own proprietary means for calculating “viewability” scores. Despite commonalities, no two vendors net the same score. It’s up to advertisers, agencies, and their ad-tech partners to determine which viewability vendors are providing the most accurate score. These scores are not just important post-ad placement. In the programmatic space, knowing in advance if the ad is viewable is key. Vendors have rolled out pre-bid viewability targeting as a result, and programmatic buying platforms should have this capability.

Viewability is not an issue for advertisers and ad-tech companies to solve alone. Publishers are reacting to the demand for more viewable inventory. Some are redesigning their websites to promise higher numbers of ads in view, even guaranteeing viewable space.

Should advertisers only buy from exchanges or publishers guaranteeing viewable inventory? Of course not, and especially not in programmatic buying. Viewability is only part of the equation in making a decision of when and where to place an ad. If you have a segment of highly responsive users, like shopping cart “abandoners,” it might still be a high-value ad buy even if the viewability score is not at 100 percent. You risk missing a potential convertor, and possibly at a lower CPM, for the sake of one metric like viewability.

For clarity, just because an impression does not get scored as viewable — or doesn’t have a high probability of being viewable — doesn’t mean the user didn’t view the impression. We recognize that this is a confusing issue because viewability is based on a variety of methodologies that often include some consideration of probability. So when buying in the programmatic space, viewability is not necessarily the primary score that should be used, although it can certainly be a valuable metric.

There is a false conflation of viewability and fraud — meaning that sometimes the market has believed that when an impression is viewable, it is by nature not fraudulent. This is not the case, and it’s important to understand that the two things are not the same. Fraudsters have become wise to the fact that markets care about viewable ads, leading to a new type of fraud: view fraud. They can easily manipulate web behavior in order to pad a flimsy viewability metric. In fact, a 2014 study by White Ops reveals that when examining the viewability score of fraudulent entities versus real humans, the fraud actually exhibited a higher viewability.

In the programmatic space, we can swiftly push aside view fraud as an issue by optimizing to real advertiser outcomes, which are harder to manipulate. Optimizing to outcomes such as product purchases or validated account sign-ups are two great examples. Even for brand marketers, or those without online purchases to measure, a curated list of other success metrics can be used; downloads, email submissions, content viewed on site, time spent on site, and pages viewed are all such examples. These are much better success indicators than viewability.

In either case, a marketer can introduce A/B testing in order to see what benefit the media served had over an unexposed group. This methodology is easy to implement, with one nice byproduct being that in order to optimize to real outcomes, the ad has to actually be seen in order to prove lift. Even media at a slightly lower percentage viewed can drive better performance lift if the audience is more responsive.

In addition to A/B testing, you can also consider scrubbing out any conversions where the ad was deemed unviewable. While not perfect, due to the technical limitations described earlier, the focus on the viewability-required business outcome will promote better media planning decisions for future campaigns.

Publishers understand that viewable ads are in high demand, so they charge a premium for this inventory, often in the form of private marketplace deals. In cases where publishers are charging a fixed CPM or high floor price, the money you spent to pay that premium likely overshadows any waste you might have encountered from purchasing open-market non-viewable ads. Choosing a better KPI should solve for viewability by default, but there are still some marketers fixated on 100 percent viewability as a goal.

Unfortunately, percentages don’t tell you anything about media cost. If you are able to achieve 80 percent viewability, but at half the cost, and consequently serve more of those highly responsive cart abandoners, why would we necessarily care about achieving 100 percent viewability? You may argue that the more expensive ads are more “premium” in nature, but without considering additional KPIs, what does premium even mean? In an ideal world, we’d be serving 100 percent viewable ads to every user we identify to be high value. However, due to the vendor and supply constraints described earlier, this simply isn’t feasible in the present state of the market.

The pursuit of high viewability might be worthwhile, but instead of relying simply on a percentage metric to define success, remember what outcomes you are ultimately trying to achieve. Being able to tout 100 percent viewability does not get you any closer to driving true business outcomes, and in fact, can distract you from the metrics that do correlate to your outcomes — finding the right audience, supply mix, and creative messaging, and then valuing each element appropriately.

Eric Picard is vice president of product planning for omni-channel media at MediaMath. Co-author Mike Monaco is MediaMath’s vice president of programmatic strategy and optimization, and co-author Ari Buchalter is president of technology at the company.

Why Do Web Pages Load So Slowly In A Broadband World?

By Eric Picard (Originally Published on AdExchanger.com – Wednesday, September 30th, 2015)

If you ask anyone, anywhere, if they like advertising, the answer will likely be a laugh and quick “no.” From a small number of people, you will get a virulent “hell no!”

But most people recognize that the content they consume is free because of advertising, and they have been willing to accept the quid pro quo of free content funded by advertising for nearly all media, for nearly all time. That’s changed over the last few years, and the easy installation of ad blockers – which frequently improve the experience of viewing web pages – has negatively impacted the ad-supported Internet.

We’re in this situation as an industry because we’ve abused our relationship with consumers. We’ve failed to design pages with the user experience optimized around the content first, with the advertising experience seamlessly incorporated into that content. That is not a call for native advertising. It’s a call to actually design the advertising and content experiences together – and to ensure that both work well and satisfy consumers’ need of great content for free.

What I mean here is that the page needs to load quickly, with the content loading first, followed by ads and then invisible code to track users. In addition to loading quickly, the page needs to be beautiful and have high utility for the user.

We all have had the horrible experience of tapping a click-bait link in social media that leads to a web page with a photo gallery of 20-plus images, each of which require as many as three clicks to move to the next image. Each click also leads to a new load of advertising. That’s the most egregious example of what is frustrating consumers today. It should be equally frustrating to advertisers and agencies – who are basically footing the bill for terrible experiences and likely getting no value from those ad impressions. 

Unfortunately, the mean load times of nearly all content pages on the Internet is not much better than these “bottom-of-the-barrel” sites, with a few notable exceptions. Once you move beyond the very best publishers, the cliff over which the consumer stumbles is pretty high. The vast majority of sites don’t load much faster than the very slowest.

Why has the web become a wasteland of user experiences, and why do web pages take almost as long to load today as they did back in the days of dial-up? Is this fixable?

A History Lesson

As I’m finding more often these days, we need to look back in order to look forward.

In 1997 when I started Bluestreak, one of the first rich media advertising technology companies, the bandwidth available to nearly all Internet users was dial-up constrained to either 56K baud (that’s bits audio) or even 14.4K baud. That’s worse than your worst mobile data connection today. Yet we were able to deliver amazing ad experiences. But that was almost 20 years ago.

Bluestreak’s technology was Java applet-based and designed to support the needs of low-bandwidth users at a time when publishers had extremely conservative file size restrictions on advertising.

Our initial load of image and code was less than 1 kilobyte (KB) of data, which would render an ad on a page with a message that read, “Loading.” A subsequent download of less than 5 KB would get an initial image onto the screen. The total subsequent load of a rich media banner would be less than 64 KB. And these were rich media ads – not static banners.

In 1998 we rolled out expanding banners, rich media applications with multiple pages and all sorts of “special effects” and various interactive behaviors. The following year, we launched some of the first video ads online. We made wonderful things happen for advertisers and consumers within the very tight constraints of bandwidth and file-size limits.

With bandwidth basically unlimited today, why do pages load so slowly when we proved almost 20 years ago that great ad experiences could be loaded on dial-up connections?

Solving The Problem

Publishers really own the bulk of this problem because slow page loads relate to how pages are coded. Software for delivering web pages must be optimized such that the site’s visual components and content load very quickly. This is not an overnight change – it may require entire web experiences to be recoded. Finding quality engineers in the publishing space who understand how to code pages properly is a challenge. But this is a critical and almost existential issue for publishers, and we’ve repeatedly seen how good user experiences drive up the value of pages.

To that point, advertisers and agencies need to hold publishers accountable for the user and advertising experiences. They should stop buying advertising from publishers that don’t solve this problem, or at the very least push hard on publishers to ensure that they design pages that load quickly and are not bandwidth hogs. This last part is particularly important for mobile – where the user’s data plan is being impacted by all the content being loaded on the page, including the advertising.

When buying ads programmatically, advertisers and agencies should use a technology provider like Trust Metrics or Integral Ad Science to determine if the advertising experience being provided is high-quality and brand-safe. The technology provider can scan a web page to determine if there is quality content and page layout, with a small number of ads and sufficient white space, or if the page is an “ad farm,” with dozens of ads.

Creative agencies need to design ads that load quickly and optimize file size. This means building teams with coding skills to build fast-loading HTML5 ads and working with rich media vendors to build optimized ad experiences.

Similarly, rich media ad companies need to embrace the idea that desktop web users need fast-loading ads – even if they are on broadband – and that rich experiences don’t require massive file sizes or bandwidth.

And agencies should vet these companies and ensure that they are following best practices. While desktop users typically have “all-you-can-eat” data consumption plans, that’s not the case for mobile. Many of the pages we visit on mobile are non-optimized desktop sites that load even even more slowly over mobile devices. If the consumer’s data plan takes the hit of all the ad content loading, it’s injury to insult.

Users are not blocking advertising because they hate advertising. They hate the horrendous experience of visiting terribly coded and designed web pages with too many and slow loading ads. If the experience of viewing the web using an ad blocker is significantly better because pages load faster and look better, this is purely a problem that publishers, creative agencies and rich media companies need to fix.

Our industry is the problem, not the consumer. So let’s fix it.

Why Programmatic Budgets Will See Massive Growth

By Eric Picard (Originally Published on AdExchanger.com – Wednesday, June 3rd, 2015)

There was a time when advertising was a game of statistical assumptions about the types of people who were consuming media. Television had four networks and there were only dozens of mainstream magazines, typically one local newspaper read by a large percentage of adults and various radio stations in each market.

In what is possibly the most basic truth of the media industry, the fragmentation trend has continued with a constantly growing number of media vehicles against which smaller slices of people’s time are applied.

Even when media-buying teams were specialized by media type, such as TV buyers and magazine buyers, the fragmentation problem still faced an unmanageable outcome. But digital media has blurred the lines between channels. Digital media buyers are now responsible for buying display ads on PC web, mobile web, digital video on both and, increasingly, audio ads. Channels, like in-game ads, and format variances, such as native ads, increase the complexity.

Billions of dollars have been invested in the next generation of media-buying technology over the past 10 years. As expected by those investors, the digital media space has grown incredibly.

The amount of money spent on digital on PCs has almost caught up to the amount of time spent by people consuming digital media – which means that spending “growth” is slowing on a year-over-year percentage basis. But spending is still growing at incredible rates. Mobile still has a massive growth opportunity that looks much like the “Internet” looked 10 years ago, as you can see below in Mary Meeker’s most recently updated “% of Time Spent” chart.

ericpicardchart

The New Planning And Buying

When planning and buying was tied to a small number of media channels and publishers per channel, it was reasonable for planning and buying group of 100 people to execute large budgets against a relatively small number of publishers. With fragmentation, the complexity of executing in any one channel makes this approach untenable.

And yet, the vast majority of ad dollars spent today are still spent against media that is bought the same way it was 10 years ago. Meanwhile, programmatic media-buying platforms have exploded on the scene and made it possible for one buyer to effectively input buying rules that allow for hundreds of billions of buying decisions per day. Each impression is evaluated in real time, valued against the campaign goals and only purchased if the value of the impression is higher than its price. This revolution puts the advertiser/buyer in control of defining, evaluating and valuing the ad inventory – a highly desirable transition to advertisers.

Although this is a technological miracle, these programmatic buying platforms have been relegated to only a small percentage of overall digital media budgets. Yes, programmatic is a rapidly growing percentage, but still has been largely limited to direct-response budgets until relatively recently.

It makes sense that direct-response budgets are directed toward the programmatic channel – buying platforms can evaluate audiences and apply explicitly identified audiences to a specific set of criteria, measured against explicit ROI goals. For direct-response campaigns, it’s easy to justify spending more than 20% of the media budget on programmatic because first-party data is such an obvious leap for marketers.

However, we have these amazing platforms with immense capabilities for evaluating enormous numbers of impressions per second and making intelligent decisions about which impressions to buy. And we have all sorts of bridging technologies and measurement models, such as Nielsen’s OCR and comScore’s VCE, to help drag budgets that need to move evolutionarily from the panel-based model approach to TV buying to more automated buying models. But there’s a chicken-and-egg problem that hasn’t been resolved.

While programmatic buying platforms are orders of magnitude more advanced than the old ways of buying media, planning methodologies for allocating budget ahead of the buying process simply haven’t kept up with the buying revolution. Under the current model, planners divvy up the budget to different buying teams, sending large chunks to “traditional” digital media buyers (an oxymoron if ever there was one) and smaller chunks to the programmatic buying team.

This is despite the fact that programmatic buying methodologies can execute both budgets equally efficiently and effectively. Buyers can just as effectively execute their budget for direct buys programmatically. The difference when a programmatic buying platform is used is that every impression can be evaluated against the campaign goals expressed by the planner, and either be bought or rejected. This “outcomes-based” buying actually puts the planner’s objectives right at the center of the buy – and pushes the media toward an even playing field between brand and direct response.

To execute a media plan using only direct buys today means that the old-world scale issues apply: A media-buying team of 100 people typically buys from between 50 to 60 publishers. This ratio means that in a world with millions of websites, a tiny fraction of available inventory is considered. And buying teams that only buy direct are unlikely to evaluate publishers outside of their personal experience, as is human nature. This is not to say that there is no place for direct media buys – they absolutely serve a purpose. But there are many other ways to run after any campaign objective, whether the desired outcome or goal is to drive an immediate sale or to reach a specific audience, or to reach a more general audience.

The Role Of Direct Buys In A Programmatic World

Programmatic buying teams now use mechanisms like private marketplace deals to execute direct buys with publishers, which enables buyers to establish more controls over how impressions will be selected or rejected than a direct buy. In a standard direct buy, every impression must be consumed. In a programmatic-first world, only impressions that match the campaign goals are purchased. And the role of a direct buy has more to do with ensuring that an advertiser can purchase inventory from a specific publisher that may otherwise be unavailable or in short supply over the open RTB inventory channel.

In a programmatic-first world, campaigns are begun over just open RTB. Using white lists and evaluating which publishers saw impression volume periodically can show how much inventory is available on that publisher over the exchanges. A private marketplace should be considered if a publisher is determined to be valuable and inventory volumes do not respond to increasing the bids on a CPM basis for available impressions. One way that programmatic-first buyers will make evaluations regarding private marketplace buys or even direct buys is to test on the exchange first to see if the inventory can be bought there. If standard bids aren’t finding the inventory desired on a publisher, and raising the bids doesn’t open up inventory, a private marketplace buy or direct buy is the answer. But there is a lot of value in finding that inventory on the exchange if possible.

It is sometimes the case that various business rules will render a publisher or set of desirable inventory inaccessible to a specific advertiser over the exchanges. In those cases, the issue isn’t bid price – the inventory is simply not accessible to the advertiser over the exchange without a private marketplace buy in place. These private marketplace deals will eventually replace direct buys. But in some cases, publishers may simply require a direct buy because their operations teams haven’t sorted out how to support private marketplaces or for philosophical reasons.

This last scenario is quickly evaporating from the market – buyers are increasingly demanding and receiving support for private marketplace deals across most publishers. It is not unusual for these to be part of a standard IO. For those publishers that require vendor support, the options that support programmatic sales are rapidly increasing. Publisher programmatic vendors, including Pubmatic, Casale and Rubicon, offer support for standard private marketplace buys. Google, as always, is innovating like crazy in this space. And upstarts, such as Sonobi and C1 Exchange, are examples of a new type of publisher-facing programmatic vendor that supports more flexible inventory guarantees, using programmatic pipes by integrating directly into the publisher ad server.

We’re on the cusp of a massive revolution in media planning and buying – with new tools and methodologies. There are significant advertiser and publisher benefits to sorting these issues out. But this innovation comes at a cost. Evaluating hundreds of billions of daily impressions across all these platforms, publishers, advertisers, campaigns, insertion orders, line items, placements and creatives is technologically intensive.

And while automation is often touted as a way to increase efficiency, that doesn’t mean it reduces labor costs. The number of people needed to execute this way stays static, but the salary costs go up because team members have more technical skills and are in high demand. But over time they scale exponentially better than traditional media buys. This will ultimately lead to some interesting conversations between agencies and advertisers. The procurement-driven cost-plus model is not leaving agencies room to support these newer and better ways of servicing their clients.

A better way for publishers to manage ad inventory

By Eric Picard (Originally Published on iMedia – April 16, 2015)

Publishers in general have, up until recently, thought of programmatic advertising only as a mechanism to clear unsold (remnant) inventory. Over the last few years, publishers have been able to begin integrating their programmatic sales more completely into their overall inventory pool. And those publishers that dived fully into the programmatic pool have been gathering significant learnings and gaining sustainable advantage over their competition. For those publishers who have not fully adopted programmatic methodology into their mainstream revenue operations, the time has come.

Today I’ll be using Google’s DoubleClick for Publishers and Ad Exchange as the examples of how publishers are operating. But other ad servers, SSPs, and exchange technologies support similar functionality to what I’ll describe here. I’m using Google’s because, frankly, its documentation is public, easily found via a search (shockingly), and easy to understand. If you’re using different vendors, feel free to reach out to them and ask about these concepts. I’m certain they’ll be able to accommodate you with similar approaches on their platforms.

Starting with the basics

RTB and direct make use of different infrastructure for decision-making, and ultimately it’s the publisher ad server that “owns” the direct ad sales, which controls the destiny of whether an ad impression is available to be purchased on the exchange.

Below is an example of how ad calls are made when a user visits a web browser and the page loads. This fundamental of our business should be understood before we dive into the deeper arcana of how programmatic systems interact with the publisher ad server.

When a user visits a web page, myriad events take place — most of which we’ll ignore in this article. The important thing to understand is that publishers code ad tags into their web pages, which call out to the publisher ad server. The publisher ad server returns unique identifiers to the page that tell the browser where to find the ads that have been selected.


This is how nearly all ads are served online today — and have been for more than 15 years. What’s important is how this is fulfilled under the surface of the impressions. There are numerous interactions happening within the publisher ad server, and the external systems — including standard ad platforms like third-party ad servers (DFA, Atlas, Sizmek, etc.), dynamic creative and rich media platforms (Flashtalking, PointRoll, etc.), and programmatic platforms such as supply-side platforms, ad exchanges, and demand-side platforms.

More advanced scenarios

All sorts of decisions are made in the milliseconds between the user visiting the publisher’s web page in a browser, and all of these various systems interact with each other. But we’ll leave most of these interwoven interactions aside for this discussion and keep to the critical ways that the publisher ad server interacts directly with whatever programmatic integration it has made.

Most of the time the publisher ad server interacts with an SSP (Rubicon, PubMatic, etc.) or directly with an ad exchange (Google’s AdX, AppNexus, etc.). While I’m giving examples in some parts of this article to illustrate the kinds of companies seen in the space, the reality is that the lines are very blurry, and some might argue that components of AdX and AppNexus operate like an SSP, and components of Rubicon and PubMatic operate like exchanges. Think of them as relatively interchangeable at this point.

Regardless of what vendor and mechanism is used for the programmatic supply integration (and often multiple are used), the publisher’s ad server interacts in somewhat specific ways with these systems. So let’s begin with the prioritization queue set up within the publisher ad server.

Most publisher ad servers provide functionality to allow the ad operations teams to assign the various contracts (insertion orders, or IOs) and specifically their subsequent contract line items against specific prioritization levels within the ad server. DFP has 16 levels of prioritization available, with the first 11 levels being set aside for “reserved” or “guaranteed” line items. Of these top 11, typically the first three levels are used for sponsorships — as the highest priority line items placed into the ad server.

The Fundamental Changes Happening In Programmatic Today (What ever happened to Programmatic Direct – or Automated Guaranteed?)

By Eric Picard (Originally Published on AdExchanger.com – Friday, April 3rd, 2015)

Media buying and selling have been on a slow evolutionary course since the late 1990s. With real-time bidding at the forefront, the industry has evolved rapidly since 2007, but relegated primarily to direct response on the buy side and remnant inventory sales on the sell side.

Most media sales – about 80% of digital dollars – have still been done over the direct channel, with RFPs, negotiations and inventory purchased well in advance of the campaign’s “go live” date. Despite significant growth in programmatic mobile and video, programmatic still represents a tiny fraction of dollars spent in those media channels.

While we’ve heard a lot about “automated guaranteed” over the last few years, the sector hasn’t grown as quickly as many would like. Analysts have been bullish about its growth, with Magna Global estimating that 83% of digital display spending will be “programmatic” by 2017, largely driven by the adoption of automated guaranteed and other types of “programmatic direct.”

We see the kind of growth expected in the RTB space, but not in the automated guaranteed space. Although some were gaining traction, vendors had trouble making money and several leaders were acquired last year for fairly low prices.

The amount of traction has been debated behind closed doors, with the quiet consensus emerging that automated guaranteed isn’t really taking off. The question is: Why not?

Automated Guaranteed’s Biggest Hurdles

Until a few years ago, publishers were a bit squirrelly about RTB. Sales-driven organizations doubted that RTB could provide the value that direct sales have produced for 20 years, and there was a belief that RTB drove prices downward.

Five years ago, new vendors entered the fray to focus on solving an old problem: automating the convoluted process of buying and selling direct ad campaigns. They sold publishers on this idea and created pipelines that allowed publishers to create packages that could be pushed over an API to buying tools, and would automate and streamline the human interactions that take place during a media buy.

This is a very logical path to go down – billions of dollars are spent on these direct media buys and everyone agrees that this space is incredibly inefficient. So why not just build some automation and have the whole thing streamlined and driving growth of the market?

Several factors have limited growth of the automated guaranteed space. One is that publishers have treated it like a new sales channel to shop inventory packages, not as a means to replace the standard RFP-driven direct channel. Publishers use automated guaranteed as an intermediate sales channel between direct buys and remnant sales. They have tried to use it to open up more direct buys – effectively money that was never on the table before – and entice buyers to pick up inventory packages directly instead of using RTB.

But analysts, vendors and many sales executives didn’t envision this for automated guaranteed. The goal was to push direct sales into this automated process for selling and for buyers to adopt buying tools that streamlined the RFP process. While there has been some success with this model – particularly with dedicated buying tools in the direct space – overall it has been sluggish.

To exacerbate the problem, publishers tended to package inventory for this channel in ways they’d never do if they were responding to an RFP. One media buyer I talked to about this debacle laughingly said, “They’re creating media packages and pushing them through this channel that they can’t even sell with their sales force involved. Why do they think anyone will buy it over this channel if they can’t sell it with people?”

The implication is that most buyers aren’t interested in publisher-defined inventory packages that aren’t tailored to the specific campaign goals. And what most publishers have missed is that buyers have complete control over the inventory definition when buying over the RTB channel, which has been as much a growth factor as reducing waste or lowering average CPMs. Control always wins for buyers.

These various problems with the automated guaranteed channel have slowed adoption and growth of the programmatic direct space and produced a chilling effect on investment, leading to some of the vendor exits. But this is only in the automated guaranteed portion of the “programmatic direct” channel. There are many ways the market is beginning to improve and reinvent the media buying and selling process, and automated guaranteed is only one of them.

The final major issue here is that automated guaranteed is solving an old problem without changing the nature of the thing it’s trying to solve. It has made direct buys more efficient, rather than producing radically better direct buys.

As the automated guaranteed space was inventing itself, the RTB channel evolved much faster than anyone expected. Publishers began to begrudgingly admit that RTB wasn’t a “race to the bottom” as many feared and instead was driving significant revenue at a significantly lower cost of sales. As skepticism and suspicion evaporated, publishers have become open to bigger and broader uses of RTB.

RTB Growth

RTB has given us more value than direct buys and helped us find ways to radically improve upon direct buys. The largest vendors in the space, particularly DoubleClick and the closely related AdX exchange, rapidly innovated and released technologies like Enhanced Dynamic Allocation (EDA), leading publishers to experiment with the way they allowed direct and RTB demand to compete with each other over impressions in real time.

The result has been a significant increase in yield and overall rising CPMs in the RTB channel. In many cases, demand coming from RTB yields higher than direct buys. Using tools like EDA has not led to underdelivery or undercutting of direct deals.

And while “standard RTB” has grown rapidly and is now encroaching on inventory that traditionally was reserved for direct deals only, private marketplaces have been a real winner in the RTB space. While Deal ID as a mechanism for instituting a private marketplace buy has been somewhat vilified in the industry trade press, complaints are mostly unfounded. Private marketplace deals over RTB have grown incredibly fast and are poised to accelerate. We’ve also seen buying and selling tools rapidly advance and processes are becoming streamlined.

Buying teams within agency trading desks (ATDs) use various flavors of private marketplaces to enact one-to-one deals with publishers that largely replicate direct buys. They’ve also completed more extensive global deals with publishers that take advantage of the total demand they represent as an agency and share the supply among their clients. They are even using these broader deals as differentiators with their competitors.

Similarly, the demand-side platforms are creating differentiated supply deals with publishers that put them toward the top of the queue within the ad exchanges and, in some cases, help them bypass the exchange infrastructure altogether using RTB-based buying tools.

We’re now seeing that the ATD model itself is fracturing as media agencies pull the resources and capabilities of the ATDs in-house and push their media buyers to incorporate programmatic mechanisms into the standard buying process. Executives at nearly every large media agency and most holding companies are privately or even publicly stating that programmatic – primarily RTB mechanisms – will be incorporated into mainstream buying teams starting this year, if they haven’t done so already.

One major area of investment needed in order for media agencies to begin adopting RTB at large are tools for planning and executing buys that support the needs of a more “standard” media buyer. Specialists will certainly be helpful for the tools of today, but media planning and buying as a discipline is missing a significant window of insight and execution capability that could be coming from this channel. Tools for planning buys across direct and programmatic channels (RTB, private marketplaces, automated guaranteed and across various differentiated vendors) are desperately needed in our space.

As you might expect, vendors connected to the exchange infrastructure get access to data about all impressions defined by all criteria – including publisher, contextual, geographic and first- and third-party data segments. That data has yet to be unlocked for broad media planning and buying but soon will be. You might imagine this is an area where I’m spending a lot of my time.

I believe that success will be driven by broad shifts in the programmatic space, faster adoption of RTB-enabled buying and selling mechanisms, new programmatic offerings beyond RTB and tools to help less specialized buyers to be successful in programmatic.

The real reason advertising isn’t more relevant

By Eric Picard (Originally Published on iMedia – February 18, 2015)

I have been pretty publicly dismissive of the idea that we will see significant consumer value driven by ad targeting’s creation of more relevant advertising in the near future. Despite the frequent claim in the industry, I’d call this a false meme today; we don’t have nearly enough disparate messages from marketers to segment the population well enough. At the very least this future is further out simply because there are not enough advertisers spending enough money on enough distinct messages for enough distinct industry verticals, or enough products, to allow us to have enough relevant messages to show people.

Let me be clear: There are privacy issues with which we must contend. But if we step past them for the purposes of this article and look just at this issue of relevance driving value to the consumer, we have a long way to go. The current trend toward massive use of retargeting clearly isn’t hitting this mark if we just make our judgment based on anecdotal input from friends, family, and ourselves. How many times have you experienced (or been told by someone else about) the situation where you visit an online store, buy a product, and then get targeted with ads for the product you just purchased for several days afterwards on numerous websites?

Are the ads more relevant to you? Maybe. Do they add any value to you? Quite the opposite. You probably find the situation as annoying as I do. If I buy a new grill, show me products related to grilling — not the damn grill I just bought. If I buy a new pair of shoes, show me clothing or accessories related to the shoes. If I buy a new car, stop showing me ads for that car or even its competitors. Instead, show me ads related to the fact that I just bought that specific car, or even just that are relevant to a recent car buyer. But at the very least, stop wasting your money showing me the exact product I just purchased.

Frankly, there are reasons why the scenarios I suggest above aren’t happening. About 10 years ago, I had a conversation with an executive at a major publisher who was complaining about how irrelevant the ads on the website were to him. He hated the fact that he kept seeing a “toenail fungus” ad when he didn’t have toenail fungus. Instead, he would love to have seen ads for rock climbing gear, as that was his passion and he was currently looking for new gear.

I explained to him that the toenail fungus ad was creating both category and brand awareness so that if and when he eventually got toenail fungus, he’d remember that he could fix the problem. I also noted that we currently had literally not one ad from an advertiser that sold rock climbing gear available to target to him, so we could not meet his ad targeting needs in that way. This caused him pause. He finally got the point and was willing to concede that maybe he was a good target for toenail fungus ads — but that he hated the creative of the ad and found it “disgusting.” I explained that we could adjust the creative acceptance policy of the site to deal with that issue editorially and that maybe the ad would be more effective if the images were less graphic.

In those days, before programmatic advertising, the solution to the problem seemed like it was just around the corner. But now, a decade later, we still haven’t solved the issue. For clarity, I do very much believe that there will be a tipping point — that as we add the infrastructure and data needed to micro-segment audiences, we will see major changes. Once we have the ability to show a high-quality ad experience and effectively segment users to put ads in front of them with the same level of segmentation as a niche magazine content experience, advertisers in the myriad niche segments of advertising will flood the digital channels with creative that can be matched to the right user. We should explore this a bit.

Consider this example: We are trying to build an advertising experience that is more relevant, and the profile of the person is a 45-year-old male suburban homeowner who is an avid golfer and sports car enthusiast, with teenagers in the house. We can probably find some number of ads that are relevant. But if we want to really add value to that person, we need to have deeper profile information with a better experience of where he is in the buying cycle for those individual areas and categorization of creative messages to help tailor the ad experience for the individual.

Example: The avid golfer. There’s a whole ecosystem around golf that could be useful in creating value to the user beyond just showing ads for golf equipment in general. For instance, if our golfer was shopping for a new driver, it would be relevant to show him ads for drivers. Or if several new clubs had been purchased recently, maybe the ads should focus on balls, bags, shoes, or clothing.

Targeting our golfer based on specific product matches are pretty obvious, but equally interesting would be if he lived in the northeast, it was winter, and he’d recently showed interest in booking a vacation. In that case, the systems should be tailoring the vacation advertising around golfing destinations. This means ads for all sorts of products and services need to be categorized by the messaging used within them such that this kind of matching could be accomplished. Similarly, tailoring ads for numerous products and services around golf should be possible and make those messages more relevant to our golfer. But obviously to make that experience work well, we’d need lots of products and services that could be tailored around the “concept” of golf. Otherwise, we’d show this poor guy the same five ads all the time.

Our systems are on the cusp of these capabilities today. In fact, some of these scenarios could be activated by specific vendors in the industry. But the capabilities need to be ubiquitous enough that marketers drive those scenarios into their advertising creative and into their media plans. So it’s a bit of a chicken-and-egg conundrum: Marketers aren’t driving these scenarios to their vendors, so the vendors haven’t yet activated the capabilities to fulfill the scenarios.

We will get there. But it could take some time.

The New Premium: How Programmatic Changes The Way Advertisers Value Inventory

By Eric Picard (Originally Published on AdExchanger.com Thursday, February 5th, 2015)

Five years ago, if I told anyone in our industry that I wanted to buy or sell “premium” inventory, we’d all picture the same thing: inventory that was bought or sold directly between a media buyer and publisher’s salesperson. Maybe it would be home page inventory or a section front, a page takeover or rich unit. Or perhaps it would just involve a specific publisher that we agreed equated to “premium.”

New programmatic technologies are radically changing how we think of inventory overall, especially the term “premium.” Inventory is no longer one- or two-dimensional – the definition has become much more complex. It is a multidimensionally defined set of attributes that includes traditionally “publisher-controlled” inputs, such as page location, dimensions of the creative, category and content adjacencies. But today there are additional overlaid attributes that flesh out the definition.

Advertisers can bring their own data to the dance, which we’ll hesitantly call “first party,” and overlay additional data sources, which we’ll hesitantly call “third party.” And beneath the surface level attributes are underlying components that can be much more dynamic. These components can help predict how effectively an impression can drive a campaign’s goals or outcomes.

Programmatic buying platforms historically were tied to open exchange inventory, but increasingly, they are used as primary buying platforms across open RTB, private marketplaces, direct publisher integrations and even to support direct buys. This more holistic approach ultimately leads to a “programmatic first” point of view, as the new inventory definitions being rightly demanded by advertisers become their starting point on media buys. While RTB “only” represents 20% to 40% of budgets today, it’s clear that the rapid growth of programmatic will drive these broader inventory definitions across the buyer-seller boundary.

Achieving Symmetry

Publishers are embracing the newly empowered media buyers, allowing them to bring their own data for direct buys. They are also allowing buyers to connect directly to their ad servers for programmatically enabled direct buys and buy-side inventory decisioning in real time. For the past few years, the asymmetry of information in programmatic – publishers had no idea why advertisers bought their inventory on the exchange – has been a sore point.

Publishers point out that if buyers work with them, they can open paths to the inventory, inclusive of audiences, that buyers are looking for on the exchange. As we see more collaboration between buyers and sellers on these points, pockets of highly valuable inventory that were lying dormant inside the publisher’s ad server (dare we say “premium”) will suddenly open up.

To use a mining analogy, publishers previously sold unrefined chunks of ore to media buyers, who found a variety of metals inside, but only some of it was valuable to them. So buyers started buying inventory through other marketplaces that allowed them to use their own tools and data to locate the chunks of ore that contained the metals they cared about. Now publishers are saying, “If you’re willing to pay us what you think that metal is worth, we can find more of it than you’re getting on those secondary marketplaces. But you have to work with us to get access to it.”

This new approach is both exciting and refreshing. The industry is getting over old suspicions and reluctance to share information. The asymmetry is becoming more symmetrical, and everyone involved gets more value. Days are still early, and only the most advanced players are figuring out how to make this work, but it won’t be long before this new way of defining “premium” is the standard.

Evolving Definition

How do we define “premium” in this new programmatically enabled world? Premium inventory matches the advertiser’s holistic goals, inclusive of where the ad will run – publisher, category, page location or format – and the multidimensional profiles of anonymous users behind the impressions, including first- and third-party audience data definitions, as well as geographic, demographic and other data elements provided by publishers and other parties. The advertiser believes the premium inventory will help fulfill their goals and drive outcomes that they desire.

That’s a mouthful, eh? How about this: Premium inventory matches the goals of the advertiser well enough that they’re willing to pay a premium for access.